Travis E. Gibson

Universality of human microbial dynamics

The recent realization that human-associated microbial communities play a crucial role in determining our health and well-being1,2 has led to the ongoing development of microbiome-based therapies3 such as fecal microbiota transplantation4,5. Thosemicrobial communities are very complex, dynamic6 and highly personalized ecosystems3,7, exhibiting a high degree of inter-individual variability in both species assemblages8 and abundance profiles9. It is not known whether the underlying ecological dynamics, which can be parameterized by growth rates, intra- and inter-species interactions in population dynamics models10, are largely host-independent (i.e. “universal”) or host-specific. If the inter-individual variability reflects host-specific dynamics due to differences in host lifestyle11, physiology12, or genetics13, then generic microbiome manipulations may have unintended consequences, rendering them ineffectual or even detrimental. Alternatively, microbial ecosystems of different subjects may follow a universal dynamics with the inter-individual variability mainly stemming from differences in the sets of colonizing species7,14. Here we developed a novel computational method to characterize human microbial dynamics. Applying this method to cross-sectional data from two large-scale metagenomic studies, the Human Microbiome Project9,15 and the Student Microbiome Project16, we found that both gut and mouth microbiomes display pronounced universal dynamics, whereas communities associated with certain skin sites are likely shaped by differences in the host environment. Interestingly, the universality of gut microbial dynamics is not observed in subjects with recurrent Clostridium difficile infection17 but is observed in the same set of subjects after fecal microbiota transplantation. These results fundamentally improve our understanding of forces and processes shaping human microbial ecosystems, paving the way to design general microbiome-based therapies18.

Adaptive control of hypersonic vehicles in the presence of modeling uncertainties

This paper proposes an adaptive controller for a hypersonic cruise vehicle subject to aerodynamic uncertainties, center-of-gravity movements, actuator saturation, failures, and time-delays. The adaptive control architecture is based on a linearized model of the underlying rigid body dynamics and explicitly accommodates for all uncertainties. It also includes a baseline proportional integral filter commonly used in optimal control designs. The control design is validated using a high-fidelity HSV model that incorporates various effects including coupling between structural modes and aerodynamics, and thrust pitch coupling. An elaborate comparative analysis of the proposed Adaptive Robust Controller for Hypersonic Vehicles (ARCH) is carried out using a control verification methodology. In particular, we study the resilience of the controller to the uncertainties mentioned above for a set of closed-loop requirements that prevent excessive structural loading, poor tracking performance and engine stalls. This analysis enables the quantification of the improvements that result from using and adaptive controller for a typical maneuver in the V - h space under cruise conditions.

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

Significance Large-scale biological network analyses often use concepts used in social networks analysis (e.g. finding “communities,” “hubs,” etc.). However, mathematically advanced engineering concepts have only been applied to analyze small and well-characterized networks so far in biology. Here, we applied a sophisticated engineering tool, from control theory, to analyze a large-scale directed human protein–protein interaction network. Our analysis revealed that the proteins that are indispensable, from a network controllability perspective, are also commonly targeted by disease-causing mutations and human viruses or have been identified as drug targets. Furthermore, we used the controllability analysis to prioritize novel cancer genes from cancer genomic datasets. Altogether, we demonstrated an application of network controllability analysis to identify new disease genes and drug targets. The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

On Adaptive Control With Closed-Loop Reference Models: Transients, Oscillations, and Peaking

One of the main features of adaptive systems is an oscillatory convergence that exacerbates with the speed of adaptation. Recently, it has been shown that closed-loop reference models (CRMs) can result in improved transient performance over their open-loop counterparts in model reference adaptive control. In this paper, we quantify both the transient performance in the classical adaptive systems and their improvement with CRMs. In addition to deriving bounds on L-2 norms of the derivatives of the adaptive parameters that are shown to be smaller, an optimal design of CRMs is proposed that minimizes an underlying peaking phenomenon. The analytical tools proposed are shown to be applicable for a range of adaptive control problems including direct control and composite control with observer feedback. The presence of CRMs in adaptive backstepping and adaptive robot control is also discussed. Simulation results are presented throughout this paper to support the theoretical derivations.

Adaptive Control of Hypersonic Vehicles in the Presence of Thrust and Actuator Uncertainties

This paper proposes an adaptive controller for a hypersonic cruise vehicle subjected to thrust and actuator uncertainties. The controller is derived using a nonlinear dynamics model of a generic airbreathing hypersonic vehicle, which has been addressed by Bolender, Doman, and their coworkers. The model is physics based, incorporates compressibility eects of air through Prandtl-Meyer and shock theory, elastic eects, and the coupling between the airframe and combustion system. Several uncertainties are introduced in this model that mimic thrust loss, actuator saturation, as well as loss of control eectiveness. An adaptive controller that eectively accomplishes command following under cruise conditions in the presence of these uncertainties is developed. The robustness of this controller is guaranteed in the presence of disturbances, unmodeled dynamics, and actuator saturation. Simulation results using the high-delity model are provided to illustrate the properties of the adaptive robust controller.

Improved Transient Response in Adaptive Control Using Projection Algorithms and Closed Loop Reference Models

This paper explores the properties of adaptive systems with closed–loop reference models. Historically, reference models in adaptive systems run open–loop in parallel with the plant and controller, using no information from the plant or controller to alter the trajectory of the reference system. Closed–loop reference models on the other hand use information from the plant to alter the reference trajectory. Using the extra design freedom available in closed–loop reference models, we design adaptive controllers that are (a) stable, and (b) have improved transient properties. Numerical studies that complement theoretical derivations are also reported.

Adaptive Output Feedback Based on Closed-Loop Reference Models

This technical note presents the design and analysis of an adaptive controller for a class of linear plants in the presence of output feedback. This controller makes use of a closed-loop reference model as an observer, and guarantees global stability and asymptotic output tracking.

Design and Verification of an Adaptive Controller for the Generic Transport Model

This paper focuses on the development, implementation, and verification of An Adaptive Control Technology for Safe Flight (ACTS). In particular, we design a controller for the Generic Transport Model (GTM) and evaluate the robustness improvements resulting from adaptation when various uncertainties and failures occur. The ACTS architecture consists of three major components (i) a baseline controller that provides satisfactory performance under nominal flying conditions, (ii) an adaptive controller that accommodates for anomalous flying conditions resulting from uncertainty and failure, and (iii) a nonlinear reference model customized according to the GTM dynamics. While the baseline controller uses anti-wind up devices and a control allocation scheme that correlates inputs of the same class, the adaptive controller accommodates for control saturation and integration wind-up without enforcing any allocation, thereby enabling the generation of independent inputs. The effectiveness of the ACTS controller is studied by evaluating its performance for a set of damages in the aircraft’s structure, and by carrying out control verification studies that evaluate the degradation in closed-loop performance resulting from failures of increasing levels of severity.

On the Origins and Control of Community Types in the Human Microbiome

Microbiome-based stratification of healthy individuals into compositional categories, referred to as “enterotypes” or “community types”, holds promise for drastically improving personalized medicine. Despite this potential, the existence of community types and the degree of their distinctness have been highly debated. Here we adopted a dynamic systems approach and found that heterogeneity in the interspecific interactions or the presence of strongly interacting species is sufficient to explain community types, independent of the topology of the underlying ecological network. By controlling the presence or absence of these strongly interacting species we can steer the microbial ecosystem to any desired community type. This open-loop control strategy still holds even when the community types are not distinct but appear as dense regions within a continuous gradient. This finding can be used to develop viable therapeutic strategies for shifting the microbial composition to a healthy configuration.

Trustable autonomous systems using adaptive control

A long standing problem in adaptive control is the derivation of robustness properties in the presence of unmodeled dynamics, a necessary and highly desirable property for designing adaptive flight control for systems with trustable autonomy. We provide a solution to this problem in this paper for linear time-invariant plants whose states are accessible for measurement. This is accomplished by using a Lipschitz continuous projection algorithm that allows the utilization of properties of a linear system when the adaptive parameter lies on the projection boundary. This in turn helps remove the restriction on plant initial conditions, as opposed to the currently existing proofs of semi-global stability. A direct implication of this result is the robustness of adaptive control systems to time-delays, and the guarantee that the underlying adaptive system will have a delay margin.